@bentossell Good question! At my previous company we spent a massive amount of development effort building and maintaining our data warehouse, but still our data analysts were frustrated: Pipelines would often fall behind, and they would have to wait for free dev cycles to get access to new data sources. I looked around and saw many companies were having the same problem. I thought there must be a better way. Etleap lets data analysts and scientists create data pipelines through our web UI. The data pipelines pull data from any source, and data transformations can be specified through an intuitive data wrangler UI. The transformed data is loaded into their Redshift data warehouse, where analysts can use the tools they know and love to do their analysis. Etleap takes care of operating the data pipeline over time, and provides simple workflows for handling issues that arise, like schema changes and parsing errors.

@mopatches Analytics teams need to integrate data from different sources - some are structured (e.g. SQL DBs, Salesforce) and some are semi-structured (log files, CSVs). Etleap's approach is to let you interact with the raw data to set up your transformations, in a way that's similar to Stanford's data wrangler (http://vis.stanford.edu/wrangler/). In addition to the usual suspects like splitting and extracting, we support common operators like parsing JSON objects/arrays and XML. Check out the screenshot at the top for an example!